Book Image

Mastering Go - Second Edition

By : Mihalis Tsoukalos
Book Image

Mastering Go - Second Edition

By: Mihalis Tsoukalos

Overview of this book

Often referred to (incorrectly) as Golang, Go is the high-performance systems language of the future. Mastering Go, Second Edition helps you become a productive expert Go programmer, building and improving on the groundbreaking first edition. Mastering Go, Second Edition shows how to put Go to work on real production systems. For programmers who already know the Go language basics, this book provides examples, patterns, and clear explanations to help you deeply understand Go’s capabilities and apply them in your programming work. The book covers the nuances of Go, with in-depth guides on types and structures, packages, concurrency, network programming, compiler design, optimization, and more. Each chapter ends with exercises and resources to fully embed your new knowledge. This second edition includes a completely new chapter on machine learning in Go, guiding you from the foundation statistics techniques through simple regression and clustering to classification, neural networks, and anomaly detection. Other chapters are expanded to cover using Go with Docker and Kubernetes, Git, WebAssembly, JSON, and more. If you take the Go programming language seriously, the second edition of this book is an essential guide on expert techniques.
Table of Contents (20 chapters)
Title Page


In statistics and machine learning, classification is the process of putting elements into existing sets that are called categories. In machine learning, classification is considered a supervised learning technique, which is where a set that is considered to contain correctly identified observations is used for training before working with the actual data.

A very popular and easy-to-implement classification method is called k-nearest neighbors (k-NN). The idea behind k-NN is that we can classify data items based on their similarity with other items. The k in k-NN denotes the number of neighbors that are going to be included in the decision, which means that k is a positive integer that is usually pretty small.

The input of the algorithm consists of the k-closest training examples in the feature space. An object is classified by a plurality vote of its neighbors,...